kh jddlmZddlmZmZmZddlmZmZddl m Z m Z ddl m Z mZmZddlmZddlmZd d lmZmZmZd d lmZd d lmZmZd dlmZmZm Z gdZ!Gdde Z"GddeZ#GddeZ$ededdfdddddeee$efd e%d!e%d"ed#e#f d$Z&y)%)partial)AnyOptionalUnion)nnTensor) DeQuantStub QuantStub)InvertedResidualMobileNet_V2_Weights MobileNetV2)Conv2dNormActivation)ImageClassification)register_modelWeights WeightsEnum)_IMAGENET_CATEGORIES)_ovewrite_named_paramhandle_legacy_interface) _fuse_modules _replace_reluquantize_model)QuantizableMobileNetV2MobileNet_V2_QuantizedWeights mobilenet_v2cTeZdZdededdffd ZdedefdZd deeddfd Z xZ S) QuantizableInvertedResidualargskwargsreturnNcjt||i|tjj |_yN)super__init__r quantizedFloatFunctionalskip_addselfr!r" __class__s g/var/www/teggl/fontify/venv/lib/python3.12/site-packages/torchvision/models/quantization/mobilenetv2.pyr'z$QuantizableInvertedResidual.__init__s) $)&) 446 xc|jr+|jj||j|S|j|Sr%)use_res_connectr*addconvr,r0s r.forwardz#QuantizableInvertedResidual.forwards8   ==$$Q ! 5 599Q< r/is_qatc tt|jD][}t|j|tj us,t |jt|t|dzg|d]y)NrTinplace)rangelenr4typerConv2drstr)r,r7idxs r. fuse_modelz&QuantizableInvertedResidual.fuse_model"s]TYY( YCDIIcN#ryy0dii#c(CaL)A6SWX Yr/r% __name__ __module__ __qualname__rr'rr6rboolrA __classcell__r-s@r.r r sJ7c7S7T7  F Y$Y4Yr/r cTeZdZdededdffd ZdedefdZd deeddfd Z xZ S) rr!r"r#Nc`t||i|t|_t |_y)zq MobileNet V2 main class Args: Inherits args from floating point MobileNetV2 N)r&r'r quantr dequantr+s r.r'zQuantizableMobileNetV2.__init__)s) $)&)[ "} r/r0cl|j|}|j|}|j|}|Sr%)rK _forward_implrLr5s r.r6zQuantizableMobileNetV2.forward4s1 JJqM   q ! LLOr/r7c|jD]G}t|turt|gd|dt|tus7|j |Iy)N)012Tr9)modulesr=rrr rA)r,r7ms r.rAz!QuantizableMobileNetV2.fuse_model:sK %AAw..a&$GAw55 V$  %r/r%rBrHs@r.rr(sG %c %S %T %F %$%4%r/rcjeZdZedeedddeddejdd d d id d dd Z e Z y)rzOhttps://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth) crop_sizeiz5)rrqnnpackzUhttps://github.com/pytorch/vision/tree/main/references/classification#qat-mobilenetv2z ImageNet-1Kg'1Q@gV@)zacc@1zacc@5g$C?gMb @z These weights were produced by doing Quantization Aware Training (eager mode) on top of the unquantized weights listed below. ) num_paramsmin_size categoriesbackendrecipe unquantized_metrics_ops _file_size_docs)url transformsmetaN) rCrDrErrrrr IMAGENET1K_V1IMAGENET1K_QNNPACK_V1DEFAULTr/r.rrBs_# ].#>!. m/==##   0$Gr/rquantized_mobilenet_v2)name pretrainedcf|jddrtjStjS)NquantizeF)getrrgr rf)r"s r.rpbs+ ::j% (5JJ ! / /r/)weightsNTF)rqprogressrnrqrrrnr"r#c |rtntj|}|Kt|dt |j dd|j vrt|d|j d|j dd}tddti|}t||r t|||"|j|j|d|S) a Constructs a MobileNetV2 architecture from `MobileNetV2: Inverted Residuals and Linear Bottlenecks `_. .. note:: Note that ``quantize = True`` returns a quantized model with 8 bit weights. Quantized models only support inference and run on CPUs. GPU inference is not yet supported. Args: weights (:class:`~torchvision.models.quantization.MobileNet_V2_QuantizedWeights` or :class:`~torchvision.models.MobileNet_V2_Weights`, optional): The pretrained weights for the model. See :class:`~torchvision.models.quantization.MobileNet_V2_QuantizedWeights` below for more details, and possible values. By default, no pre-trained weights are used. progress (bool, optional): If True, displays a progress bar of the download to stderr. Default is True. quantize (bool, optional): If True, returns a quantized version of the model. Default is False. **kwargs: parameters passed to the ``torchvision.models.quantization.QuantizableMobileNetV2`` base class. Please refer to the `source code `_ for more details about this class. .. autoclass:: torchvision.models.quantization.MobileNet_V2_QuantizedWeights :members: .. autoclass:: torchvision.models.MobileNet_V2_Weights :members: :noindex: num_classesr[r\rXblockT)rr check_hashri) rr verifyrr<repoprr rrload_state_dictget_state_dict)rqrrrnr"r\models r.rr^sX19,>RZZ[bcGfmSl9S5TU  $ !&)W\\)5L MjjI.G " O)D O OE%ug& g44hSW4XY Lr/)' functoolsrtypingrrrtorchrrtorch.ao.quantizationr r torchvision.models.mobilenetv2r r r ops.miscrtransforms._presetsr_apirrr_metar_utilsrrutilsrrr__all__r rrrFrrir/r.rs''8^^,677(C?? Y"2Y"%[%4$K$8-. 0 UY 3 e9;OOP Q33 3  3  3/3r/